Application of Multi-Criteria Decision Analysis to Identify Global and Local Importance Weights of Decision Criteria

One of the main challenges in the Multi-Criteria Decision Analysis (MCDA) field is how we can identify criteria weights correctly. However, some MCDA methods do not use an explicitly defined vector of criterion weights, leaving the decision-maker lacking knowledge in this area. This is the motivation for our research because, in that case, a decision-maker cannot indicate a detailed justification for the proposed results. In this paper, we focus on the problem of identifying criterion weights in multi-criteria problems. Based on the proposed Characteristic Object Method (COMET) model, we used linear regression to determine the global and local criterion weights in the given situation. The work was directed toward a practical problem, i.e., evaluating Formula One drivers' performances in races in the 2021 season. The use of the linear regression model allowed for identifying the criterion weights. Thanks to that, the expert using the system based on the COMET method can be equipped with the missing knowledge about the significance of the criteria. The local identification allowed us to establish how small input parameter changes affect the final result. However, the local weights are still highly correlated with global weights. The proposed approach to identifying weights proved to be an effective tool that can be used to fill in the missing knowledge that the expert can use to justify the results in detail. Moreover, weights identified in that way seem to be more reliable than in the classical approach, where we know only global weights. From the research it can be concluded, that the identified global and local weights importance provide highly similar results, while the former one provides more detailed information for the expert. Furthermore, the proposed approach can be used as a support tool in the practical problem as it guarantees additional data for the decision-maker.